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Journal ArticleDOI

Pseudoreplication and the Design of Ecological Field Experiments

Stuart H. Hurlbert
- 01 Feb 1984 - 
- Vol. 54, Iss: 2, pp 187-211
TLDR
Suggestions are offered to statisticians and editors of ecological journals as to how ecologists' under- standing of experimental design and statistics might be improved.
Abstract
Pseudoreplication is defined. as the use of inferential statistics to test for treatment effects with data from experiments where either treatments are not replicated (though samples may be) or replicates are not statistically independent. In ANOVA terminology, it is the testing for treatment effects with an error term inappropriate to the hypothesis being considered. Scrutiny of 176 experi- mental studies published between 1960 and the present revealed that pseudoreplication occurred in 27% of them, or 48% of all such studies that applied inferential statistics. The incidence of pseudo- replication is especially high in studies of marine benthos and small mammals. The critical features of controlled experimentation are reviewed. Nondemonic intrusion is defined as the impingement of chance events on an experiment in progress. As a safeguard against both it and preexisting gradients, interspersion of treatments is argued to be an obligatory feature of good design. Especially in small experiments, adequate interspersion can sometimes be assured only by dispensing with strict random- ization procedures. Comprehension of this conflict between interspersion and randomization is aided by distinguishing pre-layout (or conventional) and layout-specifit alpha (probability of type I error). Suggestions are offered to statisticians and editors of ecological j oumals as to how ecologists' under- standing of experimental design and statistics might be improved.

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Journal ArticleDOI

A new method for non-parametric multivariate analysis of variance

TL;DR: In this article, a non-parametric method for multivariate analysis of variance, based on sums of squared distances, is proposed. But it is not suitable for most ecological multivariate data sets.
Book

Experimental Design and Data Analysis for Biologists

TL;DR: An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data is as discussed by the authors, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models Multivariate techniques, including classification and ordination, are then introduced.
Journal ArticleDOI

A protocol for data exploration to avoid common statistical problems

TL;DR: A protocol for data exploration is provided; current tools to detect outliers, heterogeneity of variance, collinearity, dependence of observations, problems with interactions, double zeros in multivariate analysis, zero inflation in generalized linear modelling, and the correct type of relationships between dependent and independent variables are discussed; and advice on how to address these problems when they arise is provided.
BookDOI

Soil Sampling and Methods of Analysis

TL;DR: In this paper, the authors present a set of methods for soil sampling and analysis, such as: N.H.Hendershot, H.M.Hettiarachchi, C.C.De Freitas Arbuscular Mycorrhiza, Y.K.Soon and W.J.
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Modeling Survival and Testing Biological Hypotheses Using Marked Animals: A Unified Approach with Case Studies

TL;DR: A recent survey of capture-recapture models can be found in this article, with an emphasis on flexibility in modeling, model selection, and the analysis of multiple data sets.
References
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Book

Statistical Principles in Experimental Design

TL;DR: In this article, the authors introduce the principles of estimation and inference: means and variance, means and variations, and means and variance of estimators and inferors, and the analysis of factorial experiments having repeated measures on the same element.
Journal ArticleDOI

Statistical Principles in Experimental Design

TL;DR: This chapter discusses design and analysis of single-Factor Experiments: Completely Randomized Design and Factorial Experiments in which Some of the Interactions are Confounded.
Journal ArticleDOI

Principles and Procedures of Statistics.

Book

Statistical Methods for Research Workers

R. A. Fisher
TL;DR: The prime object of as discussed by the authors is to put into the hands of research workers, and especially of biologists, the means of applying statistical tests accurately to numerical data accumulated in their own laboratories or available in the literature.